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相关概念视频

Randomized Experiments01:13

Randomized Experiments

6.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.4K
Random and Systematic Errors01:20

Random and Systematic Errors

10.8K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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相关实验视频

Updated: Jun 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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对随机组合进行了外推交叉验证.

Jin-Hong Du1,2, Pratik Patil3, Kathryn Roeder1

  • 1Department of Statistics and Data Science, Carnegie Mellon University.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|October 23, 2024
PubMed
概括
此摘要是机器生成的。

外推交叉验证 (ECV) 有效地调整随机组合参数,如组合和子样本大小. 与传统的交叉验证技术相比,这种新的方法实现了接近最佳的预测准确性,计算成本较低.

关键词:
包装包装包装包装包装包装包装包装包装包装包装分布式学习是一种分布式的学习.组合学习组合学习随机的森林随机的森林风险额外推算 风险额外推算调音和模型选择的选择.

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科学领域:

  • 机器学习 机器学习
  • 计算生物学 计算生物学
  • 统计建模 统计建模

背景情况:

  • 集体方法,包括包装和随机森林,广泛应用于各种科学领域.
  • 集成参数的高效调整仍然是一个重大挑战,尽管它们的流行.
  • 现有的交叉验证方法可能是计算密集型或对参数调整不理想的.

研究的目的:

  • 引入额外推算交叉验证 (ECV) 以优化随机组合中的集体和子样本大小.
  • 开发一种在参数调节中实现高精度和计算效率的方法.
  • 解决对高维数据和计算约束的有效调整策略的需求.

主要方法:

  • 在小集成尺寸的初始估计器中使用袋外误差.
  • 采用一种基于预测风险分解的新风险推断技术.
  • 建立组合和子样本大小的风险推断的统一一致性.

主要成果:

  • 对于二次预测风险,ECV产生了-optimal组合,接近预言调整的性能.
  • 该方法在包括高维设置在内的各种集合和子样本大小中证明了理论一致性.
  • 在一个预测表面蛋白质丰度的案例研究中,ECV的表现优于样本分割和k折交叉验证.

结论:

  • ECV提供了一种计算效率高,准确的方法来调随机组合.
  • 该方法在理论上很强大,可以适应一般预测因素和轻微时刻假设.
  • 在计算约束下,ECV为复杂的生物数据分析中的参数优化提供了实用解决方案.